Chemical looping (CL) is a recently developed process which can be utilized in electrical power generation plants which burn fuels such as coal, biomass, and other opportunity fuels. The CL process can be implemented in existing or new power plants, and provides promising improvements in terms of reduced plant size, reduced emissions, and increased plant operational efficiency, among other benefits.
A typical CL system utilizes a high temperature process, whereby solids such as calcium- or metal-based compounds, for example, are “looped” between a first reactor, called an oxidizer, and a second reactor, called a reducer. In the oxidizer, oxygen from air injected into the oxidizer is captured by the solids in an oxidation reaction. The captured oxygen is then carried by the oxidized solids to the reducer to be used for combustion and/or gasification of a fuel such as coal, for example. After a reduction reaction in the reducer, the solids, no longer having the captured oxygen, are returned to the oxidizer to be oxidized again, and the cycle repeats.
The CL process is more complicated than processes of traditional plants such as conventional circulating fluidized bed (CFB) plants, for example. In particular, control of circulating solids in the CL process requires multi-loop interactive flow and inventory controls which are not required in traditional plants. As a result, traditional plant controls applied to the CL process necessarily result in separate control loops for each CL loop. However, using separate control loops for each CL loop is inefficient and does not optimize performance of the CL process, since accurate control depends on coordinated control between individual loops. Thus, interactions between variables for each loop of the CL process have to be taken into account to optimize overall CL process performance.
In addition, the CL process has multi-phase flows and chemical reactions which are characterized by process nonlinearities and time delays due to mass transport and chemical reaction rates. As a result, traditional power plant design without considering control optimization systems in early stages of process design is further inadequate for integrated optimization of process performance and system operability.
Optimization tools which have been developed thus far are focused on optimizing conventional combustion power plants. As a result, these optimization tools have been focused on solving very specific, localized optimization problems rather than global optimization of complex plant operations. Additionally, statistical analysis methods associated with optimization of conventional combustion power plants is based upon an assumption of linear relationships between variables. As a result, these statistical analysis methods are cumbersome and inaccurate when used to analyze the complex, inter-related, nonlinear dynamics of variables in the CL process.
In the next generation power plants based on a CL system, steam-water side control requirements will remain essentially the same as in current conventional plants (e.g. feedwater and steam flows, steam pressures, steam temperatures, drum levels). However, it is expected that improved controls which utilize both steam-water side variables and combustion/gasification CL variables will be required to better handle inherent process variable interactions in the CL process. In addition, conventional power plant simulators are limited to steam/water side process dynamics and only very simple combustion or furnace process dynamics are modeled; dynamic models of complex atmosphere control systems such as in the CL process are not available at this time. Neural network (NN) modeling has been used for conventional power plant simulators, but implementing this approach for a CL-based power plant has thus far required a prohibitive amount of time and effort to collect the required amount of statistically significant test data to develop a validated NN model for the more complex process dynamics associated with the CL plant.
Accordingly, it is desired to develop a control and optimization system and, more particularly, an integrated control and optimization system for a chemical looping process, which overcomes the shortfalls described above.